code: def forward(self, g_list):
      feature_list = []
      for g in g_list:
          feature_list.append(g.ndata["feat"])
      for i in range(self.num_layers):
          W = self.gcn_weights_list[i]
          for j, g in enumerate(g_list):
              X_tilde = self.pooling_layers[i](feature_list[j])
              W = self.recurrent_layers[i](W, X_tilde)
              feature_list[j] = self.gnn_convs[i](
                  g, feature_list[j], weight=W
              )
      return self.mlp(feature_list[-1])
  
imports: import torch
  import torch.nn
  from dgl.nn.pytorch import GraphConv
  from torch.nn import init
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 3 for calling the EvolveGCNH obj's underlying member method code with example usage: obj.forward(g_list) and returns: self